Faculty of Management and Economics, Kaetsu University, 2-8-4 Minami-cho, Hanakoganei, Kodaira-shi, Tokyo, 187-8578, Japan.
Sci Rep. 2020 Mar 16;10(1):4814. doi: 10.1038/s41598-020-61562-6.
The control mechanisms and implications of heart rate variability (HRV) under the sympathetic (SNS) and parasympathetic nervous system (PNS) modulation remain poorly understood. Here, we establish the HR model/HRV responder using a nonlinear process derived from Newton's second law in stochastic self-restoring systems through dynamic analysis of physiological properties. We conduct model validation by testing, predictions, simulations, and sensitivity and time-scale analysis. We confirm that the outputs of the HRV responder can be accepted as the real data-generating process. Empirical studies show that the dynamic control mechanism of heart rate is a stable fixed point, rather than a strange attractor or transitions between a fixed point and a limit cycle; HR slope (amplitude) may depend on the ratio of cardiac disturbance or metabolic demand mean (standard deviation) to myocardial electrical resistance (PNS-SNS activity). For example, when metabolic demands remain unchanged, HR amplitude depends on PNS to SNS activity; when autonomic activity remains unchanged, HR amplitude during resting reflects basal metabolism. HR parameter alterations suggest that age-related decreased HRV, ultrareduced HRV in heart failure, and ultraelevated HRV in ST segment alterations refer to age-related decreased basal metabolism, impaired myocardial metabolism, and SNS hyperactivity triggered by myocardial ischemia, respectively.
心率变异性（HRV）在交感神经系统（SNS）和副交感神经系统（PNS）调节下的控制机制及其意义仍知之甚少。在这里，我们通过对生理特性的动态分析，在随机自恢复系统中从牛顿第二定律推导出的非线性过程中建立了 HR 模型/HRV 响应者。我们通过测试、预测、模拟以及敏感性和时间尺度分析来验证模型。我们确认 HRV 响应者的输出可以被接受为真实的数据生成过程。实证研究表明，心率的动态控制机制是一个稳定的平衡点，而不是奇怪的吸引子或平衡点和极限环之间的转换；心率斜率（幅度）可能取决于心脏干扰或代谢需求平均值（标准差）与心肌电阻（PNS-SNS 活动）的比值。例如，当代谢需求保持不变时，心率幅度取决于 PNS 对 SNS 活动的影响；当自主活动保持不变时，静息时的心率幅度反映基础代谢。心率参数的变化表明，与年龄相关的 HRV 降低、心力衰竭时的超低 HRV 和 ST 段改变时的超高 HRV 分别对应于与年龄相关的基础代谢降低、心肌代谢受损以及心肌缺血引发的 SNS 过度活跃。
